Network embedding aims at learning an effective vector transformation for entities in a network. We observe that there are two diverse branches of network embedding: for homogeneous graphs and for multi-relational graphs. This paper then proposes MARINE, a unified embedding framework for both homogeneous and multi-relational networks to preserve both the proximity and relation information. We also extend the framework to incorporate existing features of nodes in a graph, which can further be exploited for the ensemble of embedding. Our solution possesses complexity linear to the number of edges, which is suitable for large-scale network applications. Experiments conducted on several real-world network datasets, along with applications in link prediction and multi-label classification, exhibit the superiority of our proposed MARINE.

Research of social recommendation aims at exploiting social information to improve the quality of a recommender system. It can be further divided into two classes. Explicit social recommendation assumes the existence of not only the users' ratings on items, but also the explicit social connections between users. Implicit social recommendation assumes the availability of only the ratings but not the social connections between users, and attempts to infer implicit social connections between users with the goal to boost recommendation accuracy. This paper proposes a unified framework that is applicable to both explicit and implicit social recommendation. We propose an optimization framework to learn the degree of social correlation and rating prediction jointly, so these two tasks can mutually boost the performance of each other. Furthermore, a well-known challenge for implicit social recommendation is that it takes quadratic time to learn the strength of pairwise connections. This paper further proposes several practical tricks to reduce the complexity of our model to be linear to the observed ratings. The experiments show that the proposed model, with only two parameters, can significantly outperform the state-of-the-art solutions for both explicit and implicit social recommender systems.

We investigate an unsupervised generative approach for network embedding. A multi-task Siamese neural network structure is formulated to connect embedding vectors and our objective to preserve the global node ranking and local proximity of nodes. We provide deeper analysis to connect the proposed proximity objective to link prediction and community detection in the network. We show our model can satisfy the following design properties: scalability, asymmetry, unity and simplicity. Experiment results not only verify the above design properties but also demonstrate the superior performance in learning-to-rank, classification, regression, and link prediction tasks.

PageRank has been the signature unsupervised ranking model for ranking node importance in a graph. One potential drawback of PageRank is that its computation depends only on input graph structures, not considering external information such as the attributes of nodes. This work proposes AttriRank, an unsupervised ranking model that considers not only graph structure but also the attributes of nodes. AttriRank is unsupervised and domain-independent, which is different from most of the existing works requiring either ground-truth labels or specific domain knowledge. Combining two reasonable assumptions about PageRank and node attributes, AttriRank transfers extra node information into a Markov chain model to obtain the ranking. We further develop approximation for AttriRank and reduce its complexity to be linear to the number of nodes or links in the graph, which makes it feasible for large network data. The experiments show that AttriRank outperforms competing models in diverse graph ranking applications.

We study the fundamental k-nearest neighbor (kNN) search problem on distributed time series. A server has constantly received various reference time series Q of length X and seeks the exact kNN over a collection of time series distributed across a set of M local sites. When X and M are large, and when the amount of query increases, simply sending each Q to all M sites incurs high communication bandwidth costs, which we would like to avoid. Prior work has presented a communication-efficient kNN algorithm for the Euclidean distance similarity measure. In this paper, we present the first communication-efficient kNN algorithm for the dynamic time warping (DTW) similarity measure, which is generally believed a better measure for time series. To handle the complexities of DTW, we design a new multi-resolution structure for the reference time series, and multi-resolution lower bounds that can effectively prune the search space. We present a new protocol between the server and the local sites that leverages multi-resolution pruning for communication efficiency and cascading lower bounds for computational efficiency. Empirical studies on both real-world and synthetic data sets show that our method reduces communication bandwidth by up to 92%.

It is shown that any Reed-Solomon (RS) decoder which corrects both errors and erasures also can be used as an encoder for the RS code. This technique eliminates the need of a separate subsystem, namely the RS encoder in either two-way digital communication systems or storage devices which use RS encoding and decoding. As a consequence a single RS decoder chip which corrects both errors and erasures can be mass-manufactured for use in all two-way communication systems and storage devices such as a digital video-cassette recorder (VCR) or a write-once, read-many, (WORM) optical disk drive.>